Abstract
Presented here is a Bayes factor active sonar (BFAS) inference scheme for high-frequency broadband decisions from relatively short vertical arrays. The Bayes factor properly accounts for environmental information regarding the refractive media, as well as surface and volume reverberation models. The BFAS addresses acoustic scatterer depth uncertainty through proper marginalization rather than maximization as employed in the generalized likelihood ratio test. The BF operates as a proper aggregation of a set of time-varying quadratic forms in beam-delay space, optimally balancing target, reverberation, and noise subspaces. As the minimum average risk processor it optimally attenuates reverberation subspaces while preserving the scattering body subspace, effectively increasing signal-to-reverberation-+noise ratios (SRNR) despite depth uncertainty. Depth-invariant modes (DIM) are leveraged for a computationally fast BFAS factorization. Performance testing across various refractive and shallow water environments is demonstrated and lends credence to the approach. [Funded by the Office of Naval Research (ONR321US)].